Causal Intervention. The third category of work is quite related to our work, which mainly focused on reducing the spurious correlations with causal intervention. Wang et al. used causal intervention to better learn the region features of the object detection task [1]}. When trying to learn the conditional probability between two objects like "toilet" and "person", they assumed other objects as confounders and learned the objective of \(p(\text{\emph {toilet}} | \text{do}(\text{\emph {person}}))\)  by cutting off the link between other co-occurred objects and the object "person". Yang et al. assumed the dataset as the confounder and adjust it as concepts, such as "apple" and "green". Then they cut off the link between these concepts and image features.
